Distribution Systems describe the networks that deliver energy, data, or resources from centralized nodes to end users. In power grids, this involves routing electricity efficiently and securely. At NECLA, distribution system research combines optimization, sensing, and AI to improve resiliency, detect anomalies, and integrate renewable energy. Similar principles are applied to optical and data distribution systems, ensuring robustness and sustainability in critical infrastructure.

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Integration of Fiber Optic Sensing and Sparse Grid Sensors for Accurate Fault Localization in Distribution Systems

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Fault localization in power distribution networks is essential for rapid recovery and enhancing system resilience. While Phasor Measurement Units (PMUs or ?PMUs) providehigh-resolution measurements for precise fault localization, their widespread deployment is cost-prohibitive. Distributed Fiber Optic Sensing (DFOS) offers a promising alternative for event detection along power lines using collocated optical fiber; however, it cannot independently differentiate between events and pinpoint exact fault locations. This paper introduces an innovative framework that combines DFOS with sparsely deployed PMUs for accurate fault localization. The proposed approach first utilizes a Graph Attention Network (GAT) model to capture spatial and temporal correlations from synchronized PMU and DFOS measurements, effectively identifying fault zones. High-spatial- resolution DFOS measurements further refine the fault locationwithin the identified zone. Singular Value Decomposition (SVD) is applied to extract feature vectors from DFOS measurements, enhancing the convergence speed of the GAT model. Thisintegrated solution significantly improves localization accuracy while minimizing reliance on extensive deployment of PMUs.

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